Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Coherent phrase model for efficient image near-duplicate retrieval
IEEE Transactions on Multimedia
Computer Vision: Algorithms and Applications
Computer Vision: Algorithms and Applications
Second ACM international workshop on multimedia in forensics, security and intelligence (MiFor 2010)
Proceedings of the international conference on Multimedia
Texton theory revisited: A bag-of-words approach to combine textons
Pattern Recognition
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Image retrieval from large databases, such as popular social networks, collections of surveillance images and videos, or digital investigation archives, is a very important task for a number of applications. In digital investigation, hashing techniques are commonly used to index large quantities of images to detect copies from different archives. In the last few years, a number of image hashing techniques based on the Bags of Visual Words paradigm have been proposed. Recently, this paradigm has been augmented by using multiple descriptors (Bags of Visual Phrases) to exploit the coherence between different feature spaces. In this paper we propose to further improve the Bags of Visual Phrases approach exploiting the coherence between feature spaces not only in the image representation, but also in the codebooks generation. Experiments performed on real and synthetic near duplicate image datasets show the effectiveness of the proposed approach, which outperforms the original Bags of Visual Phrases approach.